Data Science Final Project

library(tidyverse)
library(viridis)
library(ggridges)
library(patchwork)
library(readxl)
library(leaflet)
library(plotly)

Data Cleaning

years_1 <- c(1900:2012, 2014)
years_2 <- c(2015:2019)

importing_data = function(x){
 
  if(str_detect(x, str_c(years_1, collapse = "|"))) {
  read_csv(x, na = c("NULL", "", "0"), col_types = "cicccciiiicc") 
  } 
  
  else if(str_detect(x, str_c(years_2, collapse = "|"))){
    read_csv(x, na = c("NULL", "", "0"), col_types = "cccicccccccccccccccccciiiiccc")
  }
}

boston_df <- 
  tibble(list.files("data", full.names = TRUE)) %>% 
  setNames("file_name") %>% 
  mutate(data = map(file_name, importing_data)) %>% 
  unnest(data) %>% 
  mutate(year = readr::parse_number(file_name),
         city = coalesce(city, residence),
         display_name = str_replace_all(display_name, "[^a-zA-Z0-9]", " ")) %>% 
  mutate(country_residence = replace(country_residence, country_residence == "AHO", "Netherland Antilles")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ALB", "Albania")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ALG", "Algeria")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "AND", "Andorra")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ARG", "Argentina")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Argenti", "Argentina")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "AUS", "Australia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Austral", "Australia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "AUT", "Austria")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BAH", "Bahamas")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BAR", "Barbados")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Barbado", "Barbados")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BDI", "Burundi")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BLR", "Belarus")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BEL", "Belgium")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BER", "Bermuda")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BRA", "Brazil")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BRN", "Brunei")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BUL", "Bulgaria")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "CAN", "Canada")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "CAY", "Cayman")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "CHI", "Chile")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "CHN", "China")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "COL", "Colombia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Colombi", "Colombia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "CRC", "Costa Rica")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Costa R", "Costa Rica")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "CRO", "Croatia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "CYP", "Cyprus")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "CZE", "Czech Republic")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Czech R", "Czech Republic")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "DEN", "Denmark")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "DOM", "Dominican Republic")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Dominic", "Dominican Republic")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ECU", "Ecuador")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "EGY", "Egypt")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "El Salv", "El Salvador")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ESA", "El Salvador")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ESP", "Spain")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "EST", "Estonia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ETH", "Ethiopia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Ethiopi", "Ethiopia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Faroe I", "Faroe Islands")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "FIN", "Finland")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "FLK", "Falkland Islands")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "FRA", "France")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "GBR", "England")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "GER", "Germany")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "GRE", "Greece")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "GRN", "Greenland")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "GUA", "Guatemala")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Guatema", "Guatemala")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "HKG", "Hong Kong")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Hong Ko", "Hong Kong")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "HON", "Honduras")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Hondura", "Honduras")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "HUN", "Hungary")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "INA", "Indonesia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Indones", "Indonesia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "IND", "India")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "IRL", "Ireland")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ISL", "Iceland")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ISR", "Israel")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ITA", "Italy")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "JAM", "Jamaica")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "JPN", "Japan")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "JOR", "Jordan")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "KEN", "Kenya")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "KOR", "Korea")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Korea,", "Korea")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "KSA", "Saudi Arabia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "KUW", "Kuwait")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "LAT", "Latvia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "LIE", "Liechtenstein")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Liechte", "Liechtenstein")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Lithuan", "Lithuania")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "LTU", "Lithuania")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "LUX", "Luxembourg")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Luxembo", "Luxembourg")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Macao S", "Macao")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Macedon", "Macedonia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Malaysi", "Malaysia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "MAR", "Martinique")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Martini", "Martinique")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "MAS", "Malaysia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "MEX", "Mexico")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "MGL", "Mongolia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "MLT", "Malta")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "NCA", "Nicaragua")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "NED", "Netherlands")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Netherl", "Netherlands")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "New Zea", "New Zealand")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "NGR", "Nigeria")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "NOR", "Norway")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "NZL", "New Zealand")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "OMA", "Oman")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "PAK", "Pakistan")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Palesti", "Palestine")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "PAN", "Panama")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "PAR", "Paraguay")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Paragua", "Paraguay")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "PER", "Peru")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "PHI", "Philippines")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Philipp", "Philippines")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "POL", "Poland")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "POR", "Portugal")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Portuga", "Portugal")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Puerto", "Puerto Rico")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "QAT", "Qatar")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ROU", "Romania")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Saudi A", "Saudi Arabia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "SIN", "Singapore")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Singapo", "Singapore")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "SLO", "Slovenia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Slovaki", "Slovakia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Sloveni", "Slovenia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "SMR", "San Marino")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "South A", "South Africa")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "SRB", "Serbia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Sri Lan", "Sri Lanka")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "SUI", "Switzerland")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "SVK", "Slovakia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "SWE", "Sweden")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Switzer", "Switzerland")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "TCA", "Turks and Caicos")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "THA", "Thailand")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Thailan", "Thailand")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "TPE", "Taipei")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "TRI", "Trinidad")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Trinida", "Trinidad")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "TUR", "Turkey")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "TWN", "Taiwan")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "UAE", "United Arab Emirates")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "UGA", "Uganda")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "UKR", "Ukraine")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "United", "United States")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "URU", "Uruguay")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "USA", "United States")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "VEN", "Venezuela")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Venezue", "Venezuela")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "VGB", "Virgin Islands")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "VIE", "Vietnam")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ZIM", "Zimbabwe")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "RSA", "South Africa")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "RUS", "Russia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Russian", "Russia")) %>%
  filter(!is.na(display_name)) %>% 
  select(-file_name, -residence, -first_name, -last_name)

SUHANI

Geographical Analysis of Winners

Overview

---

We were interested in creating a map with the locations where winners from the Boston marathon over the past 120 year are from. Additionally, we wanted to examine how the locations of winners changed over time. We also analyzed data from winners from the wheel chair division and compared results to the men and women open divsion.

Data Cleaning

---

boston_df2 = boston_df %>% 
  filter(year > 1999) %>% 
  filter(overall == 1) %>% 
  filter(gender == "M") %>% 
  select(year, city, state, overall, everything()) %>% 
  drop_na(city) %>% 
  separate(city, into = c("city", "state", "country"), sep = ",") %>% 
  select(-country, -state) %>% 
  writexl::write_xlsx("interactive_map_men.xlsx")

boston_df3 = boston_df  %>% 
  filter(year > 1999) %>% 
  filter(gender_result == 1) %>% 
  filter(gender == "F") %>% 
  select(year, city, state, overall, everything()) %>% 
  drop_na(city) %>% 
  writexl::write_xlsx("interactive_map_women.xlsx")

map_df = read_excel("data/latitude_longitude_winners.xlsx", sheet = 1) %>% 
  select(year, city, latitude, longitude, age, gender, official_time, display_name) %>% 
  rename(place = city) 

map_df2 = read_excel("data/latitude_longitude_winners.xlsx", sheet = 2) %>% 
  select(year, city, latitude, longitude, age, gender, official_time, display_name) %>% 
  rename(place = city) 

men_open = read_excel("data/geo_winners.xlsx", sheet = 4) %>% 
  janitor::clean_names() %>% 
  rename(place = country) %>% 
  separate(official_time, into = c("data", "official_time"), sep = " (?=[^ ]+$)") %>% 
  select(-data) 

lat_long = read_excel("data/lat_long.xlsx") %>% 
  select(-country) %>% 
  rename(place = name)

men_merge <- merge(men_open,lat_long,by="place") %>% 
  rename(display_name = name) %>% 
  mutate(gender = "M") 

men_merge2 = men_merge %>% 
  mutate(age = NA) %>% 
  filter(year > 1900) %>% 
  filter(year < 2000) 

men_total = rbind(men_merge2, map_df)

women_open = read_excel("data/geo_winners.xlsx", sheet = 3) %>% 
  janitor::clean_names() %>% 
  rename(place = country) %>% 
  separate(official_time, into = c("data", "official_time"), sep = " (?=[^ ]+$)") %>% 
  select(-data) 

women_merge = merge(women_open, lat_long, by="place") %>% 
  rename(display_name = name) %>% 
  mutate(gender = "F") 
  
women_merge2 = women_merge %>% 
  mutate(age = NA) %>% 
  filter(year > 1900) %>% 
  filter(year < 2000) 

woman_total = rbind(women_merge2, map_df2) %>% 
  drop_na(latitude)

women_wheelchair = read_excel("data/geo_winners.xlsx", sheet = "women_wheel_chair") %>% 
    janitor::clean_names() %>% 
    separate(official_time, into = c("data", "official_time"), sep = " (?=[^ ]+$)") %>% 
    select(-data) %>% 
    mutate(gender = "F")

men_wheelchair = read_excel("data/geo_winners.xlsx", sheet = "men_wheel_chair") %>% 
    janitor::clean_names() %>% 
    separate(official_time, into = c("data", "official_time"), sep = " (?=[^ ]+$)") %>% 
    select(-data) %>% 
    mutate(gender = "M")

wheelchair_merge = rbind(women_wheelchair, men_wheelchair) %>% 
  rename(place = country)

wheelchair_total = merge(wheelchair_merge, lat_long, by="place") %>% 
  filter(!(year == 2013))

winners_bind = rbind(women_merge, men_merge)

Plots

---

Map of Male Winners

map_winners = leaflet(men_total) %>% 
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addMarkers(lat = ~latitude, lng = ~longitude,
  popup = paste("Name:", men_total$display_name, "<br>", "Year:", men_total$year,"<br>",  "Official Time:", men_total$official_time, "<br>", "Age:", men_total$age, "<br>", "Gender:", men_total$gender), clusterOptions = markerClusterOptions())

map_winners

The continent with the largest nuber of male winners from 1900 and beyond is North American, specifically the US, followed by Africa. There are 44 winners from the United States, 20 from Kenya, 10 from Japan.

---

Map of Female Winners

map_winners_women = leaflet(woman_total) %>% 
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addMarkers(lat = ~latitude, lng = ~longitude,
  popup = paste("Name:", woman_total$display_name, "<br>", "Year:", woman_total$year,"<br>",  "Official Time:", woman_total$official_time, "<br>", "Age:", woman_total$age, "<br>", "Gender:", woman_total$gender), clusterOptions = markerClusterOptions())

map_winners_women

This is in contrast to female winners from 1900 and beyond where Africa is the continent with the most winners, closely followed by Europe, and North America. Most of the female winners in Africa are from Kenya. The map for female winners suggests that a participants proximinity to Boston likely does not play a role in their probability of winning the marathon.

---

Plot of Officials Times of Winners by Year in Open Division

plot2 = winners_bind %>%
  mutate(text_label = 
           str_c("Name: ", display_name, "\nGender: ", gender)) %>% 
  plot_ly(
    x = ~year, y = ~official_time, text = ~text_label, color = ~place,
    type = "scatter") %>% 
  layout(
    title = "Official Times of Male and Female Winners each Year by Winner's Country",
    xaxis = list(title = 'Year'),
    yaxis = list(title = 'Marathon Time'), 
    legend = list(title=list(text='Residence')))

plot2

Over time, the winning time for both male and female winners has declined. While prior to 1950, most of the winners were from the United States, most of the winners are from Kenya. Additionally, we can see that the lowest marathon times have been from people from Kenya.

---

Map of Winners in Wheel Chair Division

map_wheelchair = leaflet(wheelchair_total) %>% 
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addMarkers(lat = ~latitude, lng = ~longitude,
  popup = paste("Name:", wheelchair_total$name, "<br>", "Year:", wheelchair_total$year,"<br>", "Official Time:", wheelchair_total$official_time, "<br>", "Gender:", wheelchair_total$gender), clusterOptions = markerClusterOptions())

map_wheelchair

When assessing wheel chair division map, we can see that 25 winners are from the United States, followed by 19 from Europe, and 10 from Africa. While the male and female open division lack winners from Switzerland, there are 15 winners in the wheel chair division from Switzerland.

---

Plot of Official Times of Winners by Year in Wheel Chair Division

plot3 = wheelchair_total %>%
  mutate(text_label = 
           str_c("Name: ", name, "\nGender: ", gender)) %>% 
  plot_ly(
    x = ~year, y = ~official_time, text = ~text_label, color = ~place,
    type = "scatter") %>% 
  layout(
    title = 'Official Times of Winners each Year in Wheel Chair Division',
    xaxis = list(title = 'Year'),
    yaxis = list(title = 'Marathon Time'), 
    legend = list(title=list(text='Residence')))

plot3

While there is a downward trend in official times for the open division, there is no trend in official times over time. Many of the winners pre-2000 are from the United States while many of the winners post-2000 are from South Africa. The lowest time recorded is from an individual in Switzerland.

---

Weather Analysis

library(ggplot2)

Data Cleaning

---

weather = read_excel("data/weather_conditions.xlsx") %>% 
  janitor::clean_names() %>% 
  filter(!(year == 2013)) %>% 
  separate(wind, into = c("wind_direction", "wind_speed"), sep = "\\s")  %>% 
  separate(wind_speed, into = c("wind_min", "wind_max"), sep = "-") %>% 
  mutate(wind_min = as.numeric(wind_min)) %>% 
  mutate(wind_max = as.numeric(wind_max)) %>% 
  mutate(wind_speed_average = (wind_min + wind_max)/2)

weather2 = weather %>% 
  select(boston_temp, year, wind_speed_average, sky) %>% 
  filter(!(year == 2021)) 

winners_bind2 = winners_bind  %>% 
   filter(year > 1999) 

boston_df2 = boston_df %>% 
     filter(year > 1999) 
  
merge_weather2 <- merge(weather2,winners_bind2,by="year")

merge_weather_participants = merge(weather2,boston_df2,by="year") %>% 
  mutate(boston_temp = as.numeric(boston_temp)) %>% 
  mutate(seconds = as.numeric(seconds)) 

Plots

---

Map of Weather during Boston Marathon

plot4 = weather %>%
  mutate(text_label = 
           str_c("Wind Speed Average: ", wind_speed_average)) %>%
  plot_ly(x = ~year, y = ~boston_temp, text = ~text_label, color= ~ sky, size = ~wind_speed_average,
          type = "scatter", mode = "markers",  colors = "viridis", 
          sizes = c(50, 700), marker = list(opacity = 0.7)) %>%
  layout(
    title = 'Weather over Time',
    xaxis = list(title = 'Year'),
    yaxis = list(title = 'Boston Temperature'), 
    legend = list(title=list(text='Sky')))

plot4

*Note datapoint size is related to wind speed.

Over the past 20 years, the temperature in Boston has generally stayed between 40-70 degrees and is usually a clear day. In 2012, there was a particularly hot marathon day with the tempature in the high 80’s. Over the past few years, there has been relativly low wind speeds on average with exception to 2015.

---

Wind Analysis during Boston Marathon

ggplot(data = weather)+
  geom_segment(aes(x = year, xend = year, y = wind_min, yend = wind_max, colour = wind_direction), size = 5, alpha = 0.6) + 
  labs(
    title = "Wind Speed During Boston's Marathon Over Time",
    x = "Year",
    y = "Wind Speed Range (mph)",
    color='Wind Direction')

In the early 2000’s, the wind direction was generally toward the North/north east and recently has been generally toward the west, northwest direction. There has been relatively low wind speed with little wind speed varability the past few years during the Boston marathon. Around 2010, there are relatively higher wind speeds. In 2007, there was a particularly windy marathon with speeds ranging from 20-30 mph.

---

Comparison between Chamption Times and Weather

plot5 = merge_weather2 %>%
  mutate(text_label = 
           str_c("Name: ", display_name, "\nYear: ", year, "\nResidence: place")) %>% 
 plot_ly(
    x = ~boston_temp, y = ~official_time,  text = ~text_label, color = ~sky, size = ~wind_speed_average,
          type = "scatter", mode = "markers",  colors = "viridis", 
          sizes = c(50, 700), marker = list(opacity = 0.7)) %>%
  layout(
    title = 'Champion Times and Weather',
    xaxis = list(title = "Boston's Temperature"),
    yaxis = list(title = 'Official Time'), 
    legend = list(title=list(text='Sky')))

plot5

*Note datapoint size is related to wind speed.

The fastest marathon times in the past 20 years have occured when the temperature is around 55 degrees for men and around 60 degrees for woman. At temperatures 55 and below as well as 85 and above, there tends to be higher wind speeds.

---

Comparison between Participants Times and Weather

plot6 = merge_weather_participants %>% 
   plot_ly(x = ~boston_temp, y = ~seconds, color = ~sky, type = "box") %>% 
  layout(
    title = 'Participants Times by Temperature',
    xaxis = list(title = "Boston's Temperature"),
    yaxis = list(title = 'Official Time (seconds)'), 
    legend = list(title=list(text='Sky')))

plot6

As temperature increases, the median marathon time among participants also increases also slightly increases. Furthermore, the lowested median marathon times occur around 55 degrees. We also see that there are many outliers in the higher timepoints.